
# import library pandas
import pandas as pd

# Import library numpy
import numpy as np

# Import library matplotlib dan seaborn untuk visualisasi
import matplotlib.pyplot as plt
import seaborn as sns

# me-non aktifkan peringatan pada python
import warnings 
warnings.filterwarnings('ignore')


#Panggil file (load file bernama Iris_AfterClean.csv) dan simpan dalam dataframe
dataset = 'Iris_AfterClean.csv'
iris = pd.read_csv(dataset)


# tampilkan 5 baris awal dataset dengan function head()
iris.head()


# tampilkan unique value dari species
iris['Species'].unique()

array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)


# melihat statistik data untuk data numeric dan non numeric
iris.describe(include='all')


# Melihat Informasi lebih detail mengenai struktur DataFrame dapat dilihat menggunakan fungsi info()
iris.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 140 entries, 0 to 139
Data columns (total 5 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   SepalLengthCm  140 non-null    float64
 1   SepalWidthCm   140 non-null    float64
 2   PetalLengthCm  140 non-null    float64
 3   PetalWidthCm   140 non-null    float64
 4   Species        140 non-null    object 
dtypes: float64(4), object(1)
memory usage: 5.6+ KB


# cek jumlah nilai yang hilang / missing values dari setiap kolom dengan function isnull() dan sum()
iris.isna().sum()

SepalLengthCm    0
SepalWidthCm     0
PetalLengthCm    0
PetalWidthCm     0
Species          0
dtype: int64


# cek missing values dengan visualisasi menggunakan library: Missingno adalah pustaka khusus untuk menampilkan nilai yang hilang dalam kerangka data
# jenis: barchart
import missingno as msno
msno.<.........>(<.........>,figsize=(8,6),color='skyblue')
<.........>

  File "C:\Users\ACERGC~1\AppData\Local\Temp/ipykernel_16264/816917351.py", line 4
    msno.<.........>(<.........>,figsize=(8,6),color='skyblue')
         ^
SyntaxError: invalid syntax


# visualisasi scatter plot dengan seaborn antara Sepal Length dan Sepal Width dengan parameter hue dan style='species'
g=sns.scatterplot(x='SepalLengthCm', y='SepalWidthCm', data=iris,hue='Species',style='Species')


# visualisasi scatter plot dengan seaborn antara Petal Length dan Petal Width dengan parameter hue dan style='species'
g=sns.scatterplot(x='PetalLengthCm', y='PetalWidthCm', data=iris, hue='Species', style='Species')


# buat visualisasi Pair Plots dari data iris dengan parameter hue='species'
sns.pairplot(iris, hue='Species')


plt.figure(figsize=(15,10))
plt.subplot(2,2,1)
sns.boxplot(x='Species', y='PetalLengthCm', data=iris)
plt.subplot(2,2,2)
sns.boxplot(x='Species', y='PetalWidthCm', data=iris)
plt.subplot(2,2,3)
sns.boxplot(x='Species', y='SepalLengthCm', data=iris)
plt.subplot(2,2,4)
sns.boxplot(x='Species', y='PetalWidthCm', data=iris)
plt.show()


plt.subplots(figsize=(10,7))
sns.boxplot(data=iris).set_title("Distribution of Sepal_length, Sepal_width, petal_length and petal_width of 3 flowers")
plt.show()


plt.figure(figsize=(15,10))
plt.subplot(2,2,1)
sns.violinplot(x='Species', y='PetalLengthCm', data=iris)
plt.subplot(2,2,2)
sns.violinplot(x='Species', y='PetalWidthCm', data=iris)
plt.subplot(2,2,3)
sns.violinplot(x='Species', y='SepalLengthCm', data=iris)
plt.subplot(2,2,4)
sns.violinplot(x='Species', y='PetalWidthCm', data=iris)
plt.show()


plt.subplots(figsize=(10,7))
sns.violinplot(data=iris)
plt.show()


plt.area(y=['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm'], alpha=0.4, figsize=(12, 6));


# lihat korelasi dengan function corr()
iris.corr()


# lihat korelasi dengan visualisasi heatmap
plt.figure(figsize = (8,8))
sns.heatmap(iris.corr(), annot=True,fmt="f").set_title("Corelation of attributes (petal length,width and sepal length,width) among Iris species")
plt.show()


X=iris.drop(['Species'], axis=1)
y=iris['Species']


from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
y = le.fit_transform(y)


#Metrics
from sklearn.metrics import make_scorer, accuracy_score,precision_score
from sklearn.metrics import classification_report

# Import libarary confusion matrix
from sklearn.metrics import classification_report, confusion_matrix
import itertools

from sklearn.metrics import accuracy_score ,precision_score,recall_score,f1_score

#Model Select
from sklearn.model_selection import KFold,train_test_split,cross_val_score
from sklearn.model_selection import train_test_split

from sklearn import metrics
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import f1_score

# Import libarary Logistic Regression
from sklearn.linear_model import LogisticRegression

from sklearn import linear_model
from sklearn.linear_model import SGDClassifier

# Import libarary KNN
from sklearn.neighbors import KNeighborsClassifier

# Import libarary Support Vector Machines dan linier Support Vector Machines
from sklearn import svm
from sklearn.svm import SVC

# Import libarary Gaussian Naive Bayes
from sklearn import naive_bayes


#Train and Test split
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3 ,random_state=0)


logreg = LogisticRegression(solver= 'lbfgs',max_iter=400)
logreg.fit(X_train, y_train)
Y_pred = logreg.predict(X_test)
accuracy_lr=round(accuracy_score(y_test,Y_pred)* 100, 2)
acc_log = round(logreg.score(X_train, y_train) * 100, 2)


cm = confusion_matrix(y_test, Y_pred)
accuracy = metrics.accuracy_score(y_test,Y_pred)
precision = precision_score(y_test, Y_pred,average='micro')
recall =  metrics.recall_score(y_test, Y_pred,average='micro')
f1 = metrics.f1_score(y_test,Y_pred,average='micro')

print('Confusion matrix for Logistic Regression\n', cm)
print('accuracy_Logistic Regression : %.3f' %accuracy)
print('precision_Logistic Regression : %.3f' %precision)
print('recall_Logistic Regression: %.3f' %recall)
print('f1-score_Logistic Regression : %.3f' %f1)

Confusion matrix for Logistic Regression
 [[12  0  0]
 [ 0 14  1]
 [ 0  1 14]]
accuracy_Logistic Regression : 0.952
precision_Logistic Regression : 0.952
recall_Logistic Regression: 0.952
f1-score_Logistic Regression : 0.952


knn = KNeighborsClassifier(n_neighbors = 3)
knn.fit(X_train, y_train)
Y_pred = knn.predict(X_test) 
accuracy_knn=round(accuracy_score(y_test,Y_pred)* 100, 2)
acc_knn = round(knn.score(X_train, y_train) * 100, 2)

cm = confusion_matrix(y_test, Y_pred)
accuracy = metrics.accuracy_score(y_test,Y_pred)
precision = precision_score(y_test, Y_pred,average='micro')
recall =  metrics.recall_score(y_test, Y_pred,average='micro')
f1 = metrics.f1_score(y_test,Y_pred,average='micro')


print('Confusion matrix for KNN\n', cm)
print('accuracy_KNN : %.3f' %accuracy)
print('precision_KNN : %.3f' %precision)
print('recall_KNN: %.3f' %recall)
print('f1-score_KNN : %.3f' %f1)

Confusion matrix for KNN
 [[12  0  0]
 [ 0 14  1]
 [ 0  2 13]]
accuracy_KNN : 0.929
precision_KNN : 0.929
recall_KNN: 0.929
f1-score_KNN : 0.929


plt.subplots(figsize=(20,5))
a_index=list(range(1,50))
a=pd.Series()
x=range(1,50)
#x=[1,2,3,4,5,6,7,8,9,10]
for i in list(range(1,50)):
    model = KNeighborsClassifier(n_neighbors=i) 
    model.fit(X_train, y_train) 
    prediction=model.predict(X_test)
    a=a.append(pd.Series(accuracy_score(y_test,prediction)))
plt.plot(a_index, a,marker="*")
plt.xticks(x)
plt.show()


gaussian = naive_bayes.BernoulliNB()
gaussian.fit(X_train, y_train)
Y_pred = gaussian.predict(X_test) 
accuracy_nb=round(accuracy_score(y_test,Y_pred)* 100, 2)
acc_gaussian = round(gaussian.score(X_train, y_train) * 100, 2)

cm = confusion_matrix(y_test, Y_pred)
accuracy = metrics.accuracy_score(y_test,Y_pred)
precision = precision_score(y_test, Y_pred,average='micro')
recall =  metrics.recall_score(y_test, Y_pred,average='micro')
f1 = metrics.f1_score(y_test,Y_pred,average='micro')


print('Confusion matrix for Naive Bayes\n', cm)
print('accuracy_Naive Bayes: %.3f' %accuracy)
print('precision_Naive Bayes: %.3f' %precision)
print('recall_Naive Bayes: %.3f' %recall)
print('f1-score_Naive Bayes : %.3f' %f1)

Confusion matrix for Naive Bayes
 [[ 0  0 12]
 [ 0  0 15]
 [ 0  0 15]]
accuracy_Naive Bayes: 0.357
precision_Naive Bayes: 0.357
recall_Naive Bayes: 0.357
f1-score_Naive Bayes : 0.357


from sklearn.svm import SVC
linear_svc = SVC(max_iter=4000)
linear_svc.fit(X_train, y_train)
Y_pred = linear_svc.predict(X_test)
accuracy_svc=round(accuracy_score(y_test,Y_pred)* 100, 2)
acc_linear_svc = round(linear_svc.score(X_train, y_train) * 100, 2)


cm = confusion_matrix(y_test, Y_pred)
accuracy = metrics.accuracy_score(y_test,Y_pred)
precision = precision_score(y_test, Y_pred,average='micro')
recall =  metrics.recall_score(y_test, Y_pred,average='micro')
f1 = metrics.f1_score(y_test,Y_pred,average='micro')


print('Confusion matrix for SVC\n', cm)
print('accuracy_SVC: %.3f' %accuracy)
print('precision_SVC: %.3f' %precision)
print('recall_SVC: %.3f' %recall)
print('f1-score_SVC : %.3f' %f1)

Confusion matrix for SVC
 [[12  0  0]
 [ 0 15  0]
 [ 0  2 13]]
accuracy_SVC: 0.952
precision_SVC: 0.952
recall_SVC: 0.952
f1-score_SVC : 0.952


results = pd.DataFrame({
    'Model': [ 'KNN', 
              'Logistic Regression',
              'Naive Bayes',  
              ' Support Vector Machine'],
    'Score': [ <..........>,
              <..........>,
              <..........>,  
              <..........>],
    "Accuracy_score":[accuracy_knn,
                      accuracy_lr,
                      accuracy_nb,
                      accuracy_svc
                     ]})
result_df = <..........>.sort_values(by='Accuracy_score', ascending=False)
result_df = <..........>.reset_index(drop=True)
result_df.head(9)


plt.<..........>(figsize=(12,8))
ax=<..........>.<..........>(x='Model',y="Accuracy_score",data=<..........>)
labels = (result_df["Accuracy_score"])
# add result numbers on barchart
for i, v in enumerate(<..........>):
    ax.text(i, v+1, str(v), horizontalalignment = 'center', size = 15, color = 'black')

	SepalLengthCm	SepalWidthCm	PetalLengthCm	PetalWidthCm	Species
0	4.6	3.1	1.5	0.2	Iris-setosa
1	5.0	3.6	1.4	0.2	Iris-setosa
2	5.4	3.9	1.7	0.4	Iris-setosa
3	4.9	3.1	1.5	0.1	Iris-setosa
4	5.4	3.7	1.5	0.2	Iris-setosa

	SepalLengthCm	SepalWidthCm	PetalLengthCm	PetalWidthCm	Species
count	140.000000	140.000000	140.000000	140.000000	140
unique	NaN	NaN	NaN	NaN	3
top	NaN	NaN	NaN	NaN	Iris-virginica
freq	NaN	NaN	NaN	NaN	50
mean	5.902857	3.028571	3.910714	1.262857	NaN
std	0.819365	0.398791	1.720369	0.746825	NaN
min	4.300000	2.200000	1.000000	0.100000	NaN
25%	5.200000	2.800000	1.675000	0.400000	NaN
50%	5.850000	3.000000	4.500000	1.400000	NaN
75%	6.425000	3.300000	5.100000	1.800000	NaN
max	7.900000	4.000000	6.900000	2.500000	NaN

	Model	Score	Accuracy_score
0	Logistic Regression	96.94	95.24
1	KNN	96.94	92.86
2	Naive Bayes	96.94	92.86
3	Support Vector Machine	95.92	90.48

Hands-On¶

Tugas Mandiri Pertemuan 11¶

About Iris dataset¶

The variables are:¶

Contents¶

Data Preprocessing¶

Data Visualization¶

Feature Engineering¶

Machine learning Model (Regresi Non Linier, Support Vector Machine, dll)¶

1. Data Preprocessing¶

Latihan (1)¶

Melakukan import library yang dibutuhkan¶

Load Dataset¶

Latihan (2)¶

Review Dataset¶

Latihan (3)¶

Checking if there are any missing values¶

2. Data Visualization¶

2.1 Scatter Plot¶

Latihan (4)¶

buat visualisasi scatter plot 'Sepal Length' dan 'Sepal Width'¶

buat visualisasi scatter plot 'Petal Length' dan 'Petal Width'¶

2.2 Pairplot¶

Latihan (5)¶

buat visualisasi Pair Plots dari data iris¶

2.3 BoxPlot¶

Latihan (6)¶

buat visualisasi box plot dari setiap kolom feature terhadap species¶

Latihan (7)¶

buat visualisasi box plot distribusi setiap kolom feature¶

2.4 Violin¶

Latihan (8)¶

buat visualisasi violin plot setiap kolom feature¶

Latihan (9)¶

buat visualisasi violin plot dengan swarm plot¶

2.5 Area Plot¶

Latihan (10)¶

buat visualisasi area plot pada setiap feature kolom¶

2.6 Correlation¶

Latihan (11)¶

lihat korelasi dataset dan visualisasi dengan heatmap pada feature kolom¶

Observasi :¶

Dividing data into features and labels¶

Latihan (12)¶

definisi variabel X(feature kolom) dan y(species/label):¶

Label encoding¶

Latihan (13)¶

transform label data species dengan menggunakan library LabelEncoder¶

3. Building Machine Learning Models¶

Latihan (14)¶

import library dalam kebutuhan membangun model¶

Splitting The Data into Training And Testing Dataset¶

Latihan (15)¶

split data train dan test dengan function train_test_split() dengan train_size=0.7, test_size=0.3 dan random_state=0¶

3.2 Logistic Regression:¶

Bangun model LogisticRegression dan akurasi nya¶

3.3 K Nearest Neighbor:¶

Latihan (16)¶

Bangun model KNN dan akurasi nya¶

Mari kita periksa akurasi untuk berbagai nilai n untuk Model KNN¶

3.4 Gaussian Naive Bayes:¶

Latihan (17)¶

Bangun model gaussian Naive Bayes dan akurasi nya¶

Latihan (18)¶

Bangun model gaussian Naive Bayes dan akurasi nya¶

3.5 Linear Support Vector Machine:¶

Latihan (19)¶

Bangun model Linear Support Vector Machines dan akurasi nya¶

Latihan (20)¶

Model mana yang terbaik ?¶

Visualisasikan Hasil Akurasi¶

Hasil Observasi:¶